Enterprise AI diagnosis
From scattered AI ideas to one first build.
A large enterprise had no shortage of AI ideas. What it lacked was a shared way to compare them and choose the first scenario worth funding.
AI transformation
case studies
Real projects showing how teams choose the first use case, shape the pilot, and move AI into work.
Featured case
Too many ideas. No shared bar.
Ideas were plentiful, but value, readiness, and risk were judged differently across teams.
One sprint to align and scope.
We built one evaluation frame, ranked the options, and scoped the first MVP.
Keep the first move narrow.
The first build stayed inside one human-in-the-loop workflow.
Leave behind a reusable method.
The team left with a repeatable way to score the next wave of AI work.
Leadership-ready decision material in six weeks.
The sprint delivered the framework, ranked options, MVP scope, and validation material needed for a pilot decision.
More project snapshots
Many ideas to one pilot path
Rank the ideas, tighten scope, and choose the first pilot.
Choose the platform the business can back
Compare platforms through security, cost, workflow fit, and rollout reality.
Make knowledge usable at work
Turn scattered documents into a workflow teams can actually use.
Case snapshot
From idea backlog to pilot candidate.
Best for teams with strong AI interest but no shared way to decide what comes first. We turn an idea backlog into decision material leadership can compare, rank, and act on.
Discuss a similar case- Client situation
- AI ideas were scattered and hard to compare.
- Our work
- Built the evaluation framework and tightened the MVP scope.
- Typical output
- Priority map, scenario scoring, and a validation plan.
- Best fit
- Teams preparing to turn AI interest into a first pilot wave.
Case snapshot
Choose with business, tech, and governance aligned.
Best for teams reviewing multiple AI platforms and struggling to align business fit, security, operating cost, and rollout feasibility. We turn platform debate into an evaluation model and a recommendation.
Discuss a similar case- Client situation
- Platform choices were clear technically, but not operationally.
- Our work
- Compared capability, security, cost, and workflow fit.
- Typical output
- Evaluation matrix, risk list, recommendation, and pilot scope.
- Best fit
- Teams preparing to procure, replace, or consolidate AI platforms.
Case snapshot
Turn scattered material into searchable knowledge.
Best for teams whose documents, cases, and delivery experience live across folders, chats, and people. We map the sources, usage scenarios, permission boundaries, and update path needed to make the system useful in real work.
Discuss a similar case- Client situation
- Useful material existed, but teams could not find or reuse it easily.
- Our work
- Mapped sources, retrieval paths, permissions, and workflow usage.
- Typical output
- Knowledge map, demo prototype, governance guidance, and rollout priorities.
- Best fit
- Organizations that want searchable knowledge built into delivery work.
How these engagements usually move
Define the decision, the constraint, and the first opportunity.
Get leadership, business, and delivery teams on one frame.
Shape the first prototype, tool, or MVP around real work.
Test value, adoption, and risk in one defined scenario.
Turn what works into repeatable capability.
Working through a similar decision?
Bring the scenario, pilot question, or platform choice. We'll help shape the right first move.
Contact us